Overview

Dataset statistics

Number of variables19
Number of observations50118
Missing cells5
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.3 MiB
Average record size in memory152.0 B

Variable types

Numeric8
Categorical11

Alerts

LAND has constant value "11"Constant
BEZ is highly overall correlated with LOR_ab_2021High correlation
IstFuss is highly overall correlated with UARTHigh correlation
LOR_ab_2021 is highly overall correlated with BEZHigh correlation
OBJECTID is highly overall correlated with UJAHRHigh correlation
UART is highly overall correlated with IstFussHigh correlation
UJAHR is highly overall correlated with OBJECTIDHigh correlation
UKATEGORIE is highly imbalanced (59.6%)Imbalance
IstGkfz is highly imbalanced (79.5%)Imbalance
UART has 6658 (13.3%) zerosZeros

Reproduction

Analysis started2024-06-06 12:54:41.487184
Analysis finished2024-06-06 12:55:05.249723
Duration23.76 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

OBJECTID
Real number (ℝ)

HIGH CORRELATION 

Distinct43487
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172859.25
Minimum3187
Maximum219249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size391.7 KiB
2024-06-06T14:55:05.612696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3187
5-th percentile50830.85
Q1141328.25
median197979
Q3205795.75
95-th percentile216385.15
Maximum219249
Range216062
Interquartile range (IQR)64467.5

Descriptive statistics

Standard deviation50869.617
Coefficient of variation (CV)0.29428345
Kurtosis2.3097347
Mean172859.25
Median Absolute Deviation (MAD)12991.5
Skewness-1.64585
Sum8.6633601 × 109
Variance2.5877179 × 109
MonotonicityNot monotonic
2024-06-06T14:55:06.052746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199429 2
 
< 0.1%
200476 2
 
< 0.1%
200486 2
 
< 0.1%
200485 2
 
< 0.1%
200484 2
 
< 0.1%
200483 2
 
< 0.1%
200482 2
 
< 0.1%
200481 2
 
< 0.1%
200480 2
 
< 0.1%
200479 2
 
< 0.1%
Other values (43477) 50098
> 99.9%
ValueCountFrequency (%)
3187 1
< 0.1%
3198 1
< 0.1%
3215 1
< 0.1%
3224 1
< 0.1%
3241 1
< 0.1%
3252 1
< 0.1%
3274 1
< 0.1%
3290 1
< 0.1%
3299 1
< 0.1%
3303 1
< 0.1%
ValueCountFrequency (%)
219249 1
< 0.1%
219248 1
< 0.1%
219247 1
< 0.1%
219246 1
< 0.1%
219243 1
< 0.1%
219241 1
< 0.1%
219240 1
< 0.1%
219239 1
< 0.1%
219238 1
< 0.1%
219237 1
< 0.1%

LAND
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
11
50118 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters100236
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 50118
100.0%

Length

2024-06-06T14:55:06.447230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-06T14:55:06.727381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
11 50118
100.0%

Most occurring characters

ValueCountFrequency (%)
1 100236
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100236
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 100236
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100236
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 100236
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100236
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 100236
100.0%

BEZ
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4849356
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size391.7 KiB
2024-06-06T14:55:06.992707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q38
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4895713
Coefficient of variation (CV)0.63621009
Kurtosis-1.0810325
Mean5.4849356
Median Absolute Deviation (MAD)3
Skewness0.35381828
Sum274894
Variance12.177108
MonotonicityNot monotonic
2024-06-06T14:55:07.348464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 8105
16.2%
4 6219
12.4%
2 5035
10.0%
7 4625
9.2%
3 4589
9.2%
8 3457
6.9%
9 3425
6.8%
6 3367
6.7%
12 3284
6.6%
5 3037
 
6.1%
Other values (2) 4975
9.9%
ValueCountFrequency (%)
1 8105
16.2%
2 5035
10.0%
3 4589
9.2%
4 6219
12.4%
5 3037
 
6.1%
6 3367
6.7%
7 4625
9.2%
8 3457
6.9%
9 3425
6.8%
10 2300
 
4.6%
ValueCountFrequency (%)
12 3284
6.6%
11 2675
5.3%
10 2300
 
4.6%
9 3425
6.8%
8 3457
6.9%
7 4625
9.2%
6 3367
6.7%
5 3037
6.1%
4 6219
12.4%
3 4589
9.2%

LOR_ab_2021
Real number (ℝ)

HIGH CORRELATION 

Distinct982
Distinct (%)2.0%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5706762.3
Minimum1011101
Maximum12601236
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size391.7 KiB
2024-06-06T14:55:07.709234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1011101
5-th percentile1100102
Q12500728
median5100209
Q38200725
95-th percentile12200307
Maximum12601236
Range11590135
Interquartile range (IQR)5699997

Descriptive statistics

Standard deviation3497723.7
Coefficient of variation (CV)0.6129086
Kurtosis-1.0436985
Mean5706762.3
Median Absolute Deviation (MAD)2910005
Skewness0.34798379
Sum2.8598298 × 1011
Variance1.2234071 × 1013
MonotonicityNot monotonic
2024-06-06T14:55:08.167103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100310 523
 
1.0%
1100102 348
 
0.7%
1100206 299
 
0.6%
1100309 297
 
0.6%
4100101 289
 
0.6%
1300836 284
 
0.6%
1100308 280
 
0.6%
4501042 268
 
0.5%
2200210 255
 
0.5%
5100316 254
 
0.5%
Other values (972) 47016
93.8%
ValueCountFrequency (%)
1011101 39
 
0.1%
1011102 71
0.1%
1011103 26
 
0.1%
1011104 22
 
< 0.1%
1011105 72
0.1%
1011201 78
0.2%
1011202 84
0.2%
1011203 54
0.1%
1011204 88
0.2%
1011301 106
0.2%
ValueCountFrequency (%)
12601236 37
 
0.1%
12601235 76
0.2%
12601134 27
 
0.1%
12601133 19
 
< 0.1%
12601032 50
0.1%
12601031 51
0.1%
12500930 110
0.2%
12500929 35
 
0.1%
12500928 50
0.1%
12500927 43
 
0.1%

UJAHR
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
2018
13652 
2019
13389 
2020
11810 
2021
11267 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters200472
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2018 13652
27.2%
2019 13389
26.7%
2020 11810
23.6%
2021 11267
22.5%

Length

2024-06-06T14:55:08.572192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-06T14:55:08.804409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2018 13652
27.2%
2019 13389
26.7%
2020 11810
23.6%
2021 11267
22.5%

Most occurring characters

ValueCountFrequency (%)
2 73195
36.5%
0 61928
30.9%
1 38308
19.1%
8 13652
 
6.8%
9 13389
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 200472
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 73195
36.5%
0 61928
30.9%
1 38308
19.1%
8 13652
 
6.8%
9 13389
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 200472
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 73195
36.5%
0 61928
30.9%
1 38308
19.1%
8 13652
 
6.8%
9 13389
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 200472
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 73195
36.5%
0 61928
30.9%
1 38308
19.1%
8 13652
 
6.8%
9 13389
 
6.7%

UMONAT
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8366655
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size391.7 KiB
2024-06-06T14:55:09.207025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.1699881
Coefficient of variation (CV)0.4636746
Kurtosis-0.97334956
Mean6.8366655
Median Absolute Deviation (MAD)2
Skewness-0.16843158
Sum342640
Variance10.048825
MonotonicityNot monotonic
2024-06-06T14:55:09.554035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 5476
10.9%
8 5364
10.7%
9 5238
10.5%
10 4812
9.6%
7 4592
9.2%
5 4557
9.1%
11 3980
7.9%
4 3786
7.6%
12 3311
6.6%
3 3139
6.3%
Other values (2) 5863
11.7%
ValueCountFrequency (%)
1 3118
6.2%
2 2745
5.5%
3 3139
6.3%
4 3786
7.6%
5 4557
9.1%
6 5476
10.9%
7 4592
9.2%
8 5364
10.7%
9 5238
10.5%
10 4812
9.6%
ValueCountFrequency (%)
12 3311
6.6%
11 3980
7.9%
10 4812
9.6%
9 5238
10.5%
8 5364
10.7%
7 4592
9.2%
6 5476
10.9%
5 4557
9.1%
4 3786
7.6%
3 3139
6.3%

USTUNDE
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.507283
Minimum0
Maximum23
Zeros468
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size391.7 KiB
2024-06-06T14:55:09.839126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median14
Q317
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.7457344
Coefficient of variation (CV)0.35134634
Kurtosis-0.25481648
Mean13.507283
Median Absolute Deviation (MAD)3
Skewness-0.35997228
Sum676958
Variance22.521995
MonotonicityNot monotonic
2024-06-06T14:55:10.153996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
15 4330
 
8.6%
16 4242
 
8.5%
17 4085
 
8.2%
14 3798
 
7.6%
18 3539
 
7.1%
13 3420
 
6.8%
12 3005
 
6.0%
8 2828
 
5.6%
9 2740
 
5.5%
11 2729
 
5.4%
Other values (14) 15402
30.7%
ValueCountFrequency (%)
0 468
 
0.9%
1 343
 
0.7%
2 253
 
0.5%
3 206
 
0.4%
4 206
 
0.4%
5 569
 
1.1%
6 1190
2.4%
7 2479
4.9%
8 2828
5.6%
9 2740
5.5%
ValueCountFrequency (%)
23 667
 
1.3%
22 965
 
1.9%
21 1153
 
2.3%
20 1725
 
3.4%
19 2589
5.2%
18 3539
7.1%
17 4085
8.2%
16 4242
8.5%
15 4330
8.6%
14 3798
7.6%

UWOCHENTAG
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0740253
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size391.7 KiB
2024-06-06T14:55:10.448165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7980717
Coefficient of variation (CV)0.44135016
Kurtosis-1.0837255
Mean4.0740253
Median Absolute Deviation (MAD)1
Skewness0.0043194941
Sum204182
Variance3.233062
MonotonicityNot monotonic
2024-06-06T14:55:10.819424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 8461
16.9%
4 8418
16.8%
5 8237
16.4%
2 8045
16.1%
6 7837
15.6%
7 5285
10.5%
1 3835
7.7%
ValueCountFrequency (%)
1 3835
7.7%
2 8045
16.1%
3 8461
16.9%
4 8418
16.8%
5 8237
16.4%
6 7837
15.6%
7 5285
10.5%
ValueCountFrequency (%)
7 5285
10.5%
6 7837
15.6%
5 8237
16.4%
4 8418
16.8%
3 8461
16.9%
2 8045
16.1%
1 3835
7.7%

UKATEGORIE
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
3
42413 
2
7554 
1
 
151

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50118
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 42413
84.6%
2 7554
 
15.1%
1 151
 
0.3%

Length

2024-06-06T14:55:11.181390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-06T14:55:11.466241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 42413
84.6%
2 7554
 
15.1%
1 151
 
0.3%

Most occurring characters

ValueCountFrequency (%)
3 42413
84.6%
2 7554
 
15.1%
1 151
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 42413
84.6%
2 7554
 
15.1%
1 151
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 42413
84.6%
2 7554
 
15.1%
1 151
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 42413
84.6%
2 7554
 
15.1%
1 151
 
0.3%

UART
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5307674
Minimum0
Maximum9
Zeros6658
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size391.7 KiB
2024-06-06T14:55:11.804731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q35
95-th percentile6
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1727298
Coefficient of variation (CV)0.6153704
Kurtosis-1.0320665
Mean3.5307674
Median Absolute Deviation (MAD)1
Skewness-0.21838932
Sum176955
Variance4.7207546
MonotonicityNot monotonic
2024-06-06T14:55:12.207879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 19249
38.4%
2 9245
18.4%
0 6658
 
13.3%
6 6404
 
12.8%
1 4040
 
8.1%
3 2832
 
5.7%
4 660
 
1.3%
8 490
 
1.0%
9 460
 
0.9%
7 80
 
0.2%
ValueCountFrequency (%)
0 6658
 
13.3%
1 4040
 
8.1%
2 9245
18.4%
3 2832
 
5.7%
4 660
 
1.3%
5 19249
38.4%
6 6404
 
12.8%
7 80
 
0.2%
8 490
 
1.0%
9 460
 
0.9%
ValueCountFrequency (%)
9 460
 
0.9%
8 490
 
1.0%
7 80
 
0.2%
6 6404
 
12.8%
5 19249
38.4%
4 660
 
1.3%
3 2832
 
5.7%
2 9245
18.4%
1 4040
 
8.1%
0 6658
 
13.3%

UTYP1
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8828565
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size391.7 KiB
2024-06-06T14:55:12.460216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9153487
Coefficient of variation (CV)0.49328341
Kurtosis-1.3731404
Mean3.8828565
Median Absolute Deviation (MAD)1
Skewness0.20804033
Sum194601
Variance3.6685606
MonotonicityNot monotonic
2024-06-06T14:55:12.904397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 12282
24.5%
6 11116
22.2%
3 10286
20.5%
7 4702
 
9.4%
5 4068
 
8.1%
4 3855
 
7.7%
1 3809
 
7.6%
ValueCountFrequency (%)
1 3809
 
7.6%
2 12282
24.5%
3 10286
20.5%
4 3855
 
7.7%
5 4068
 
8.1%
6 11116
22.2%
7 4702
 
9.4%
ValueCountFrequency (%)
7 4702
 
9.4%
6 11116
22.2%
5 4068
 
8.1%
4 3855
 
7.7%
3 10286
20.5%
2 12282
24.5%
1 3809
 
7.6%

ULICHTVERH
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
37495 
2
9727 
1
 
2896

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50118
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 37495
74.8%
2 9727
 
19.4%
1 2896
 
5.8%

Length

2024-06-06T14:55:13.368105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-06T14:55:13.649418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 37495
74.8%
2 9727
 
19.4%
1 2896
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 37495
74.8%
2 9727
 
19.4%
1 2896
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37495
74.8%
2 9727
 
19.4%
1 2896
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37495
74.8%
2 9727
 
19.4%
1 2896
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37495
74.8%
2 9727
 
19.4%
1 2896
 
5.8%

IstRad
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
30561 
1
19557 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50118
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30561
61.0%
1 19557
39.0%

Length

2024-06-06T14:55:14.002371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-06T14:55:14.392539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 30561
61.0%
1 19557
39.0%

Most occurring characters

ValueCountFrequency (%)
0 30561
61.0%
1 19557
39.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 30561
61.0%
1 19557
39.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 30561
61.0%
1 19557
39.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 30561
61.0%
1 19557
39.0%

IstPKW
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1
40478 
0
9640 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50118
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 40478
80.8%
0 9640
 
19.2%

Length

2024-06-06T14:55:14.694769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-06T14:55:15.073487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 40478
80.8%
0 9640
 
19.2%

Most occurring characters

ValueCountFrequency (%)
1 40478
80.8%
0 9640
 
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 40478
80.8%
0 9640
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 40478
80.8%
0 9640
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 40478
80.8%
0 9640
 
19.2%

IstFuss
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
43231 
1
6887 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50118
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 43231
86.3%
1 6887
 
13.7%

Length

2024-06-06T14:55:15.443258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-06T14:55:15.756895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 43231
86.3%
1 6887
 
13.7%

Most occurring characters

ValueCountFrequency (%)
0 43231
86.3%
1 6887
 
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43231
86.3%
1 6887
 
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43231
86.3%
1 6887
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43231
86.3%
1 6887
 
13.7%

IstKrad
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
42537 
1
7581 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50118
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 42537
84.9%
1 7581
 
15.1%

Length

2024-06-06T14:55:16.057792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-06T14:55:16.478432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 42537
84.9%
1 7581
 
15.1%

Most occurring characters

ValueCountFrequency (%)
0 42537
84.9%
1 7581
 
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 42537
84.9%
1 7581
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 42537
84.9%
1 7581
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 42537
84.9%
1 7581
 
15.1%

IstGkfz
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
48506 
1
 
1612

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50118
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 48506
96.8%
1 1612
 
3.2%

Length

2024-06-06T14:55:16.800140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-06T14:55:17.143793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 48506
96.8%
1 1612
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 48506
96.8%
1 1612
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 48506
96.8%
1 1612
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 48506
96.8%
1 1612
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 48506
96.8%
1 1612
 
3.2%

IstSonstige
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
43046 
1
7072 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50118
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 43046
85.9%
1 7072
 
14.1%

Length

2024-06-06T14:55:17.487797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-06T14:55:17.778258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 43046
85.9%
1 7072
 
14.1%

Most occurring characters

ValueCountFrequency (%)
0 43046
85.9%
1 7072
 
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43046
85.9%
1 7072
 
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43046
85.9%
1 7072
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43046
85.9%
1 7072
 
14.1%

USTRZUSTAND
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
38670 
1
11152 
2
 
296

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50118
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row2

Common Values

ValueCountFrequency (%)
0 38670
77.2%
1 11152
 
22.3%
2 296
 
0.6%

Length

2024-06-06T14:55:18.071672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-06T14:55:18.472785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 38670
77.2%
1 11152
 
22.3%
2 296
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 38670
77.2%
1 11152
 
22.3%
2 296
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 38670
77.2%
1 11152
 
22.3%
2 296
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 38670
77.2%
1 11152
 
22.3%
2 296
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 38670
77.2%
1 11152
 
22.3%
2 296
 
0.6%

Interactions

2024-06-06T14:55:01.608395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:44.373897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:46.451763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:48.558866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:50.630798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:52.945276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:55.119624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:58.176241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:55:01.937367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:44.598864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:46.718334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:48.856451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:50.915431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:53.206399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:55.375237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:58.657521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:55:02.333192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:44.861342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:46.989160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:49.074697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:51.289982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:53.470388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:55.660324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:59.480379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:55:02.515724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:45.133233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:47.251020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:49.355246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:51.598569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:53.744520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:55.933740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:59.797008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:55:02.691755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:45.399248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:47.520555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:49.575720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:51.879491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:53.931373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:56.157010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:55:00.116137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:55:02.869365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:45.656976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:47.779775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:49.845952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:52.143030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:54.251124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:56.530591image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:55:00.408206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:55:03.168912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:45.927788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:48.050459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:50.101551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:52.427919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:54.574008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:57.195386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:55:00.858486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:55:03.555573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:46.183208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:48.318354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:50.361496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:52.661762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:54.849799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:54:57.653528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-06T14:55:01.163237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-06-06T14:55:18.741952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
BEZIstFussIstGkfzIstKradIstPKWIstRadIstSonstigeLOR_ab_2021OBJECTIDUARTUJAHRUKATEGORIEULICHTVERHUMONATUSTRZUSTANDUSTUNDEUTYP1UWOCHENTAG
BEZ1.0000.0320.0270.0300.1000.1740.0370.9910.0180.0330.0100.0520.040-0.0000.020-0.0290.004-0.001
IstFuss0.0321.0000.0390.1250.1360.1720.0340.0170.0280.5190.0380.1150.095-0.0130.0550.0380.0680.008
IstGkfz0.0270.0391.0000.0400.1140.0460.0300.018-0.041-0.0330.0060.0820.042-0.0120.004-0.0850.049-0.011
IstKrad0.0300.1250.0401.0000.1300.2950.082-0.0100.011-0.1520.0100.0690.0350.0310.0070.025-0.015-0.008
IstPKW0.1000.1360.1140.1301.0000.2480.3000.0720.0060.1080.0220.0770.018-0.0200.0260.0240.0740.006
IstRad0.1740.1720.0460.2950.2481.0000.110-0.1210.006-0.0420.0480.0430.1090.0020.111-0.015-0.155-0.001
IstSonstige0.0370.0340.0300.0820.3000.1101.0000.0030.016-0.0990.0370.0110.0120.0100.015-0.0390.064-0.001
LOR_ab_20210.9910.0170.018-0.0100.072-0.1210.0031.0000.0300.0340.0860.0440.0370.0020.022-0.0280.003-0.002
OBJECTID0.0180.028-0.0410.0110.0060.0060.0160.0301.0000.0410.6970.0080.0130.2640.0310.004-0.0460.003
UART0.0330.519-0.033-0.1520.108-0.042-0.0990.0340.0411.0000.0310.1220.111-0.0200.076-0.002-0.2050.001
UJAHR0.0100.0380.0060.0100.0220.0480.0370.0860.6970.0311.0000.0090.0180.0440.0590.010-0.007-0.000
UKATEGORIE0.0520.1150.0820.0690.0770.0430.0110.0440.0080.1220.0091.0000.0360.0150.000-0.0140.0610.003
ULICHTVERH0.0400.0950.0420.0350.0180.1090.0120.0370.0130.1110.0180.0361.0000.0930.1820.328-0.0580.011
UMONAT-0.000-0.013-0.0120.031-0.0200.0020.0100.0020.264-0.0200.0440.0150.0931.0000.257-0.002-0.0060.013
USTRZUSTAND0.0200.0550.0040.0070.0260.1110.0150.0220.0310.0760.0590.0000.1820.2571.0000.013-0.047-0.006
USTUNDE-0.0290.038-0.0850.0250.024-0.015-0.039-0.0280.004-0.0020.010-0.0140.328-0.0020.0131.0000.0210.013
UTYP10.0040.0680.049-0.0150.074-0.1550.0640.003-0.046-0.205-0.0070.061-0.058-0.006-0.0470.0211.0000.001
UWOCHENTAG-0.0010.008-0.011-0.0080.006-0.001-0.001-0.0020.0030.001-0.0000.0030.0110.013-0.0060.0130.0011.000

Missing values

2024-06-06T14:55:03.951318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-06T14:55:04.795463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

OBJECTIDLANDBEZLOR_ab_2021UJAHRUMONATUSTUNDEUWOCHENTAGUKATEGORIEUARTUTYP1ULICHTVERHIstRadIstPKWIstFussIstKradIstGkfzIstSonstigeUSTRZUSTAND
02192491133701658.020211118230320101001
12192481177501134.020211219736220110001
22192471144100101.020211217435220100000
32192461144501041.020211215735210101001
4219243111111501339.02021129533600100012
52192411111100308.020211222531520100001
6219240111010200524.02021125131520100002
72192391177200409.020211220725720100002
82192381111300733.020211122332620100001
92192371122200208.02021127236420110000
OBJECTIDLANDBEZLOR_ab_2021UJAHRUMONATUSTUNDEUWOCHENTAGUKATEGORIEUARTUTYP1ULICHTVERHIstRadIstPKWIstFussIstKradIstGkfzIstSonstigeUSTRZUSTAND
501082088361166300525.020181213635300100000
501092088381111100206.020181212633600100001
501102088401188100102.020181212635300100001
501112088421122100102.020181210635300100001
50112208844111111100308.020181210639700100001
501132088461144200310.020181210635300100000
501142088481133501037.020181210635701100001
501152088501122500726.02018129632600100001
50116208851111111300618.02018129620711000001
501172088531177400927.02018127635310100001